Shuchang Zhou

Computer Vision and Pattern Recognition · with Wen · 2016–2017

A researcher in the Science corpus: 3 papers; trusts 175 researchers and is trusted by 28 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 71 citations into it; #5,590 of 21,575 by trust here, #2,625 by citations
• Artificial Intelligence — 43 citations into it; #9,666 of 35,345 by trust here, #4,370 by citations
• Efficiency & Compression — 25 citations into it; #370 of 1,264 by trust here, #207 by citations
• Model Architectures — 18 citations into it
• Media Technology — 12 citations into it; #1,736 of 3,621 by trust here, #729 by citations
• Optimization & Training — 10 citations into it

Whom Shuchang Zhou trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Yoshua Bengio
2. Diederik P. Kingma
3. Geoffrey E. Hinton
4. Alex Krizhevsky
5. Sergey Ioffe
6. Yann LeCun
7. Ilya Sutskever
8. Jimmy Ba
9. Christian Szegedy
10. Kaiming He

Who trusts Shuchang Zhou overall

1. Zhen Dong
2. Markus Nagel
3. Sungho Shin
4. Zi-Mei Yao
5. Zechun Liu
6. Yunhui Guo
7. Itay Hubara
8. Jungwook Choi
9. Kuan Wang
10. Naveen Mellempudi

Papers from Shuchang Zhou's perspective

Ordered by how strongly the researchers Shuchang Zhou trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Long Short-Term Memory
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Going deeper with convolutions
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradients (2016) — 2,283 citations
• EAST: An Efficient and Accurate Scene Text Detector (2017) — 1,675 citations
• Effective Quantization Methods for Recurrent Neural Networks (2016) — 82 citations